Data & Ai Project Manager

7 days ago

Singapore EU HOLIDAYS PTE. LTD. Full time

**Role Summary**: The Data & AI Project Manager is responsible for leading data analytics and AI initiatives across both internal and external teams, ensuring that all projects align with the agency’s strategic goals and deliver valuable insights. This role manages the lifecycle of data and AI projects, from planning and development through to implementation and optimization, working with both vendors and internal data specialists.

**Key Responsibilities**:

- **Vendor and Internal Team Coordination**:

- Manage relationships with external data and AI vendors while also leading internal data teams (local or overseas) to align on project priorities, timelines, and deliverables.
- Facilitate collaboration between internal and vendor teams, ensuring clear roles and responsibilities and efficient information flow.
- Regularly review and manage the distribution of work between internal teams and vendors to optimize resources.
- **Data Strategy and AI Project Management**:

- Develop and drive a comprehensive data and AI strategy, identifying projects that provide the highest business value.
- Define project KPIs, set clear success criteria, and establish metrics to measure the effectiveness of both in-house and outsourced data/AI initiatives.
- Ensure alignment of all data initiatives with business needs, prioritizing projects based on their impact on business performance.
- **Requirements Documentation and Solution Design**:

- Work closely with stakeholders to capture data requirements and document them into actionable project plans for internal teams and vendors.
- Provide guidance on technical requirements, data integrity, and quality standards for AI models and analytics projects.
- **Quality Assurance and Validation**:

- Oversee quality assurance for data and AI outputs, ensuring deliverables are accurate, reliable, and aligned with strategic objectives.
- Lead UAT and feedback loops with stakeholders, coordinating with both vendors and internal teams for adjustments and refinements as needed.
- **Reporting and Continuous Improvement**:

- Track and report on project outcomes, including impact assessments, user adoption metrics, and ROI for each AI and data project.
- Encourage knowledge sharing between internal and vendor teams, implementing a continuous improvement process for data practices and AI model accuracy.

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